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yashmaniya0/K-Means-Clustering-Simulator

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K-Means Clustering Simulator App

This is a Dash web application for simulating and visualizing the K-Means clustering algorithm.
Github Link

Introduction

The K-Means algorithm is an unsupervised machine learning technique used for clustering similar data points into groups or clusters. It aims to partition a dataset into K clusters, where each data point belongs to the cluster with the nearest mean (centroid). This application provides an interactive environment to understand and visualize the K-Means algorithm.

Features

How to Run

To run the K-Means Clustering Simulator App locally, follow these steps:

    Clone Repository: Clone this repository to your local machine using Git:

git clone https://github.com/yashmaniya0/K-Means-Clustering-Simulator
cd K-Means-Clustering-Simulator
pip install -r requirements.txt
python app.py

Usage

  1. Select Dataset Shape: Choose the shape of the dataset from the dropdown menu (e.g., blobs, moons, circles).
  2. Adjust Parameters: Set the dataset size, number of clusters, centroid initialization method, and maximum iterations using the sliders and dropdown menus.
  3. Generate Dataset and Centroids: Click on the "Generate Data" and "Generate Centroids" buttons to create the dataset and initialize centroids.
  4. Run Simulation: Start the simulation by clicking on the "Play" button to observe the step-by-step execution of the K-Means algorithm.
  5. Visualize Results: Watch the animation of the algorithm, explore the inertia plot, and analyze the silhouette coefficient to understand the clustering process.
  6. Interact with Animation: Use the play, pause, next step, and previous step buttons to control the animation and navigate through iterations.

Dependencies

Authors